[Background]: An emerging type of cancer treatment, known as cell immunotherapy, is gaining popularity over chemotherapy or other radia-tion therapy that causes mass destruction to our body. One favourable ap-proach in cell immunotherapy is the use of neoantigens as targets that help our body immune system identify the cancer cells from healthy cells. Neoan-tigens, which are non-autologous proteins with individual specificity, are generated by non-synonymous mutations in the tumor cell genome. Owing to its strong immunogenicity and lack of expression in normal tissues, it is now an important target for tumor immunotherapy. Neoantigens are some form of special protein fragments excreted as a by-product on the surface of cancer cells during the DNA mutation at the tumour. In cancer immunotherapies, certain neoantigens which exist only on cancer cells elicit our white blood cells (body's defender, anti-cancer T-cell) responses that fight the cancer cells while leaving healthy cells alone. Personalized cancer vaccines there-fore can be designed de novo for each individual patient, when the specific neoantigens are found to be relevant to his/her tumour. The vaccine which is usually coded in synthetic long peptides, RNA or DNA representing the neo-antigens trigger an immune response in the body to destroy the cancer cells (tumour). The specific neoantigens can be found by a complex process of biopsy and genome sequencing. Alternatively, modern technologies nowa-days tap on AI to predict the right neoantigen candidates using algorithms. However, determining the binding and non-binding of neoantigens on T-cell receptors (TCR) is a challenging computational task due to its very large search space. [Objective]: To enhance the efficiency and accuracy of traditional deep learning tools, for serving the same purpose of finding potential responsive-ness to immunotherapy through correctly predicted neoantigens. It is known that deep learning is possible to explore which novel neoantigens bind to T-cell receptors and which ones don't. The exploration may be technically ex-pensive and time-consuming since deep learning is an inherently computa-tional method. one can use putative neoantigen peptide sequences to guide personalized cancer vaccines design. [Methods]: These models all proceed through complex feature engineering, including feature extraction, dimension reduction and so on. In this study, we derived 4 features to facilitate prediction and classification of 4 HLA-peptide binding namely AAC and DC from the global sequence, and the LAAC and LDC from the local sequence information. Based on the patterns of sequence formation, a nested structure of bidirectional long-short term memory neural network called local information module is used to extract context-based features around every residue. Another bilstm network layer called global information module is introduced above local information module layer to integrate context-based features of all residues in the same HLA-peptide binding chain, thereby involving inter-residue relationships in the training process. introduced. [Results]: Finally, a more effective model is obtained by fusing the above two modules and 4 features matric, the method performs significantly better than previous prediction schemes, whose overall r-square increased to 0.0125 and 0.1064 on train and increased to 0.0782 and 0.2926 on test da-tasets. The RMSE for our proposed models trained decreased to approxi-mately 0.0745 and 1.1034, respectively, and decreased to 0.6712 and 1.6506 on test dataset. [Conclusion]: Our work has been actively refining a machine-learning model to improve neoantigen identification and predictions with the determinants for Neoantigen identification. The final experimental results show that our method is more effective than existing methods for predicting peptide types, which can help laboratory researchers to identify the type of novel HLA-peptide binding. Keywords: machine learning; Cancer Cell Immunology; HLA-peptide binding Neoantigen Prediction; HLA; Data Visualization; Novel Neoanti-gen and TCR Pairing Discovery; Vector representation
Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs
